In the past three decades, I have seen artificial
intelligence (AI) coming and going a couple of times. From studying MYCIN via
speech technology in Flanders Language Valley to today’s machine learning and
heuristics as used by Textgain from Antwerp University, the technology is here
to stay this time.
Why? Because the cost of using AI has
fallen dramatically not just in terms of hard and software but also in terms of
acquiring the necessary knowledge to master the discipline.
Yet, most of the AI initiatives are still
very much in the R&D phase or are used in limited scope. But here and
there, e.g. in big (online) retail and telecommunications, AI is gaining
traction on enterprise level. And
through APIs, open data and other initiatives, AI will become available for
smaller organisations in the near future.
To make sure this effort has a maximum
chance of success, CIOs need to embed this technology in an enterprise
architecture covering all aspects: motivations, objectives, requirements and
constraints, business processes, applications and data.
Being fully aware that I am trodding on
uncharted territory, this article is –for now- my state of the art.
Introducing AI in the capability map
AI will enhance our capabilities in all
areas of Treacy & Wiersema’s model, probably in a certain order. First comes
operational excellence as processes and procedures are easier to describe,
measure and monitor. Customer intimacy is the next frontier as the existing
discipline of customer analytics lays the foundation for smarter interactions
with customers and prospects.
The toughest challenge is in the realm of
product leadership. This is an area where creativity is key to success. There
is an approximation of creativity using what I call “property exploration”
where a dimensional model of all possible properties of a product, a service, a
marketing or production plan are mapped and an automatic cartesian product of
all levels or degrees of each property with all the other properties is
evaluated for cost and effectiveness. Sales pitch: if you want more information
about this approach, contact us.
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| Capabilities where state of the art AI can play a significant role |
Examples of capabilities where AI can play
a defining role. Some of these capabilities are already well supported, to name
a few: inventory management (automatic replenishment and dynamic storage),
cycle time management (optimising man-machine interactions), quality management
(visual inspection systems), churn management (churn prediction and avoidance
in CRM systems), yield management (price, customer loyalty, revenue and
capacity optimisation) and talent management (mining competences from CVs).
Areas where AI is coming of age: loyalty
management and competitive intelligence, R & D management and product
development.
In the next post I will discuss a generic
architecture for AI in support of primary processes; Stay tuned and… share your
insights on this topic!
